Gardnermalone9735

Recent studies have demonstrated the potential of nanoparticle-based single-ion conductors as battery electrolytes. In this work, we introduce a coarse-grained multiscale simulation approach to identify the mechanisms underlying the ion mobilities in such systems and to clarify the influence of key design parameters on conductivity. Our results suggest that for the experimentally studied electrolyte systems, the dominant pathway for cation transport is along the surface of nanoparticles, in the vicinity of nanoparticle-tethered anions. At low nanoparticle concentrations, the connectivity of cationic surface transport pathways and conductivity increase with nanoparticle loading. However, cation mobilities are reduced when nanoparticles are in close vicinity, causing conductivity to decrease for sufficiently high particle loadings. We discuss the impacts of cation and anion choice as well as solvent polarity within this picture and suggest means to enhance ionic conductivities in single-ion conducting electrolytes based on nanoparticle salts.Our quantum device is a solid-state array of semiconducting quantum dots that is addressed and read by 2D electronic spectroscopy. The experimental ultrafast dynamics of the device is well simulated by solving the time-dependent Schrödinger equation for a Hamiltonian that describes the lower electronically excited states of the dots and three laser pulses. The time evolution induced in the electronic states of the quantum device is used to emulate the quite different nonequilibrium vibrational dynamics of a linear triatomic molecule. We simulate the energy transfer between the two local oscillators and, in a more elaborate application, the expectation values of the quantum mechanical creation and annihilation operators of each local oscillator. The simulation uses the electronic coherences engineered in the device upon interaction with a specific sequence of ultrafast pulses. The algorithm uses the algebraic description of the dynamics of the physical problem and of the hardware.Doping has been used as a common method to improve photovoltaic performance in perovskite solar cells (PSCs). This paper reports a new phenomenon that the SnF2 doping can largely increase the exciton-exciton interaction through orbital magnetic dipoles toward increasing dissociation probabilities in lead-free FASnI2Br PSCs. Essentially, when orbit-orbit interaction between excitons occurs, linearly and circularly polarized photoexcitations can inevitably generate different photocurrents, giving rise to a ΔJsc phenomenon. Here, it is found that, when SnF2 doping is used to boost photovoltaic efficiency to 7.61%, the orbit-orbit interaction is increased by a factor of 2.2, shown as the ΔJsc changed from 1.21% to 0.55%. Simultaneously, magnetic field effects of Jsc indicate that increasing orbit-orbit interaction leads to an increase on the spin-orbital coupling in Sn perovskites (FASnI2Br) upon SnF2 doping. selleck inhibitor This presents a new doping effect occurring in the Sn perovskite solar cell toward enhancing photovoltaic efficiency.Understanding the photoinduced carrier dynamics in Cs2AgBiBr6 double perovskites is essential for their application in optoelectronic devices. Herein, we report an investigation on the temperature-dependent carrier dynamics in a Cs2AgBiBr6 single crystal (SC). The time-resolved photoluminescence (TRPL) measurement indicates that the majority of carriers (>99%) decay through a fast trapping process at room temperature, and as the temperature decreases to 123 K, the population of carriers with a slow fundamental decay kinetics rises to ∼50%. We show that the carrier diffusion coefficient (theoretical diffusion length) varies from 0.020 ± 0.003 cm2 s-1 (0.70 μm) at 298 K to 0.11 ± 0.010 cm2 s-1 (2.44 μm) at 123 K. However, in spite of the long diffusion length, the population of carriers that can perform long-distance transport is restricted by the trap state, which is likely a key reason limiting the performance of Cs2AgBiBr6 optoelectronic devices.The past decade has seen great progress in manipulating the structure of vapor-deposited glasses of organic semiconductors. Upon varying the substrate temperature during deposition, glasses with a wide range of density and molecular orientation can be prepared from a given molecule. We review recent studies that show the structure of vapor-deposited glasses can be tuned to significantly improve the external quantum efficiency and lifetime of organic light-emitting diodes (OLEDs). We highlight the ability of molecular simulations to reproduce experimentally observed structures, setting the stage for in silico design of vapor-deposited glasses in the coming decade. Finally, we identify research opportunities for improving the properties of organic semiconductors by controlling the structure of vapor-deposited glasses.Understanding the role of an electric field on the surface of a catalyst is crucial in tuning and promoting the catalytic activity of metals. Herein, we evaluate the oxidation of methane over a Pt surface with varying oxygen coverage using density functional theory. The latter is controlled by the electrode polarization, giving rise to the non-Faradaic modification of catalytic activity phenomenon. At -1 V, the Pt(111) surface is present, while at 1 V, α-PtO2 on Pt(111) takes over. Our results demonstrate that the alteration of the platinum oxide surface under the influence of an electrochemical potential promotes the catalytic activity of the metal oxides by lowering the activation energy barrier of the reaction.The on-the-fly generation of machine-learning force fields by active-learning schemes attracts a great deal of attention in the community of atomistic simulations. The algorithms allow the machine to self-learn an interatomic potential and construct machine-learned models on the fly during simulations. State-of-the-art query strategies allow the machine to judge whether new structures are out of the training data set or not. Only when the machine judges the necessity of updating the data set with the new structures are first-principles calculations carried out. Otherwise, the yet available machine-learned model is used to update the atomic positions. In this manner, most of the first-principles calculations are bypassed during training, and overall, simulations are accelerated by several orders of magnitude while retaining almost first-principles accuracy. In this Perspective, after describing essential components of the active-learning algorithms, we demonstrate the power of the schemes by presenting recent applications.